www.ijecs.in
International Journal Of Engineering And Computer Science ISSN:2319-7242
Volume2 Issue 8 August, 2013 Page No. 2641-2644
Trust Aware AODV
V.Vallinayagi1 Dr G.M, Nasira2
Dept of CS, SSC COLLEGE, T.N 627011,INDIA
Dept of CA, TIRUPUR, T.N 627012, INDIA
ABSTRACT:
The advantage of Mobile Ad-hoc Networks (MANETs) is to form a wireless network in the absence of fixed infrastructure. . In
defining and managing trust in a military MANET, we must consider the interactions between the composite cognitive,
social, information and communication networks, and take into account the severe resource constraints. We provide a survey of
trust management schemes developed for MANETs and discuss generally accepted classifications, potential attacks, performance
metrics, and trust metrics in MANETs. Finally, we discuss future research areas on trust management in MANETs based on the
concept of social. AODV node is able to discover multiple loop-free paths as candidates in one route discovery. These are
evaluated by two values hop count and trust values. In this paper we discuss about trust value and improves the packet ratio
Keyword: Manet,aodv trustvalue,path,time
INTRODUCTION:
In traditional wireless networks, a base station or access point
facilitate communications between nodes with the Network and
Communications with destinations outside he network. In
contrast, MANETs forms a network in the absence of fixed
infrastructures. The requirement of these networks is only
nodes that can interact with radio hard wares so as to route the
traffic using the routing protocol. Thus the reduced essential
requirement s of such networks, along with their adoptability
into tiny resource-limited devices made them more popular
and is much preferred for several applications in the area of
communications.
Routing protocols determines the nature of data forwardness
as well as
its adaptability
to
topology changes that results by mobility. Initial MANET
routing protocol like AODV [1]was not designed to withstand
malicious nodes within the network or outside attackers near
by with malicious intent. Subsequent protocols and protocol
extensions have been proposed to address the issue of security
Many of these protocols seek to apply cryptographic methods
to the existing protocols in order to secure the information in
the routing packets. This attack is very effective in MANETs,
as the devices often have limited battery power in addition to
the limited computational power.
There are two primary motivations associated with trust
management in MANETs. Firstly, trust evaluation helps
identify malicious entities. One entity can remember others
behaviors through evaluation
history. This memory provides a method for good entities to
avoid working with „ex-Convict‟ or suspected ones. Secondly,
trust management offers a prediction of one‟s future behaviors
and improves network performance. The results of evaluation
can be directly applies as an incentive for a good or honest
behavior while a penalty for a selfish or malicious behavior in
the network. The feedback reminds network participants to act
with caution.
In table II the time, storage and communication complexity
are given for different and hoc routing protocols. Time
complexity is defined as the number of steps needed to
perform a protocal operations, Storage Complexity measures
the order of the table size used by the protocols and
Communication Complexity gives the number of messages
needed to perform an operation when
an update occurs.
On Demand
Table Driven
Availability of
routing
information
Available when
needed
Periodic route
updates
Copying with
mobility
Not required
Always
available
regardless of
need
Required.
Parameters
Signaling traffic
generator
V.Vallinayagi1 IJECS Volume 2 Issue 8 August, 2013 Page No.2641-2644
Use localized
route discovery
as an ABR and
SSR.
Grows with
increasing
mobility of
Inform other
nodes to
achieve a
consistent
routing table.
Greater than
that of on –
demand routing
Page 2641
active routes.
Communica
tion
Complexity
Proto
cal
Time
Storag
Comple e
xity
Compl
exity
DSDV
O(d)
O(X)
CGSR
O(d)
O(N/M
)
WRP
O(h)
O(X*A
)
AOD
V
DSR
O(2d)
O(E)
O(2d)
O(E)
O(2N)
TORA
O(2d)
O(Dd*
A)
O(2A)
O(N)
O(N)
O(N)
O(2N)
Where,
N=Number of nodes in the network
d=Network diameter
E=Communication pairs
M=Average number of nodes in a cluster
X=Number of nodes affected by topological
Change
H=Height of routing tree
A=Average number of adjacent nodes
Dd=Number of maximum desired
Destination
2. NODE’S TRUST VALUE CALCULATION
Measuring the trust value of the node is always a
challenging problem [14&15].A nodes trustworthiness is
often related to the quality of services it provides to
others. If the quality of the service can be objectively
measured, then entities trustworthiness for that service is
called objective trust.
Most of the previous research used the approach
of subjective trust. Then they classified the trust relation
as direct and indirect relation. The direct trust relation of
a node is related to its neighbors while the indirect trust
relation is concerned with the non-neighbors.
RFN (M)(Request-for-Forwarding);The total no of
packets that node N has transmitted to node M for
Forwarding.
HFN (M) (Has-Forwarded):The total no of packets
that have been forwarded by node M and is noticed by
node N.
The two no are updated by the following rules. When
node N sends a packet to node M for forwarding, the
counter RFN (M) is increased by one. Then node N
listens to the wireless channel and check whether node
M forwards the packet as expected. If node N detects
that node M has forwarded the packet before a preset
time –out expires , the counter HFN (M) is increased by
one.
Trust value calculation basic scheme
drawbacks :(1) Increased nodes power consumption
because it assumes that each node operates in
promiscuous mode to monitor its neighbors continually.
(2)Flooding the network by broadcasting the updated
evaluations consumes the network limited band with. (3)
The broadcasted evaluation records come from
misbehaving nodes which leads to wrong results. (4)
Taking into consideration the credibility of node i which
broadcasts its evaluation record about node X when
calculating OER(X) leads to computational overhead. (5)
It does not take into account a node‟s “selective
forwarding” behavior, where it only forwards small
packets while selectively discarding larger ones.
Route’s Evaluation
Each sending node S builds its own trust
evaluation table Teval(S) using the propagated trust
values in the network. T Eval(S) contains the trust value
of all other in the network. Using these trust information,
the sending node routing agent ROA(S) is responsible
for computing the most trustworthy route to a particular
destination. If the most trustworthy route trusts value is
found lower than a threshold value (denoted by R
threshold). The route is rejected and a new Route
Discovery process is initiated. The trust value in route R
by source node S is represented as Ts(R) and given by
the following equation:
Ts(R) =min(Trustvalue(Ni))  Ni ε R (4)
3. COMPUTATION OF NODE TRUST:
The trust of a node j in another node k (node trust
for short) is a measure to ensure that packets sent by
node j have actually been forwarded by node k. Two
trust factors [CFR (t) and DFR (t)] are assigned weights
in order to determine the overall trust value of a node.
The direct trust in node k by node j is represented as Tjk
and is given by the following formula
Tjk (t)= w1 × CFR jk (t) + w2 × DFRjk (t) (2)
Where CFRjk (t) and DFRjk (t) represent control packet
forwarding ratio and data packet forwarding ratio
observed by node j for forwarding node k at time t,
respectively. The weights w1 and w2 (w1,w2 ≥ 0 and
V.Vallinayagi1 IJECS Volume 2 Issue 8 August, 2013 Page No.2641-2644
Page 2642
w1+ w2=1) are assigned to CFR and DFR, respectively.
Node k forwards the packet correctly. If so, the trust
value Tjk increases. Otherwise, Tjk decreases. In our trust
model, trust values are limited in a continuous range
from 0 to 1 (i.e. 0≤ Tjk ≤ 1). The trust value of 0
signifies complete distrust whereas the value of 1
implies absolute trust. If there is no interaction between
two nodes, the initial trust value is set to 0.75 which is
minimum trust. A threshold n termed as the blacklist
trust threshold is used to detect malicious node In other
words if the trust value is smaller than n it is regarded is
malicious nodes.
4. Proposed system:
In this paper consider trust model
Which lives on time line? Many protocols consider
certain nodes as normal and certain are malicious node
because existing node‟s
Behavior changed. so the trust will be recalculated. Each
node is given specific time in that time the node
behavior is tested
Soothe trust is calculated based on the control packets
and data packets transmission initially to find the routes
of the nodes. Trust value calculates the transmission of
data packets. So every windows calculate the number of
data packets and control packets>0
5. Experiment results:
We have conducted a comprehensive test using ns2
2.34 and all experiments are done on a pc personal
computer with pIV
The graph is shown for finding the performance activity.
When 1000*1000 grid lines, we disperse 100 nodes
Time
window
To
Max Trust obtained
T1
T2
T3
T4
T5
0.79
0.81
0.83
0.85
0.88
Table.1
Fig 2
0.75
In fig2 based on time window, the level of the trust is
incresed gradually by isolating the no of malicious nodes
from the network.
No of nodes
10
30
50
70
Malicious
nodes
4
3
10
8
Table2
Based on the number of nodes and the transmission from
one node to another node,the trust values can be deviated
in every transmission.
Conclusion
Thus this paper considering the evolution of trust
on demand adaptive trust windows model to increase the
delectability of no of malicious nodes
References:
[1] Son, B., Her, Y., Kim, J., “A Design and Implementation
of Forest-Fires Surveillance
System based on Wireless Sensor Networks for South
Korea Mountains”, IJCSNS
International Journal of Computer Science and Network
Security, vol.6 No.9B, 124–130,
September 2006.
[2] Mainwaring et al, “Wireless Sensor Networks for Habitat
Monitoring”, International Workshop on Wireless Sensor
Networks and Applications (ACM), Sep. 2002,
[3] Chintalapudi, K.; Fu, T.; Paek, J.; Kothari, N.; Rangwala,
S.; Caffrey, J.; Govindan, R.; Johnson, E.; Masri, S.,
"Monitoring civil structures with a wireless sensor network,"
Internet Computing,IEEE , vol.10, no.2, pp. 26-34, MarchApril 2006
[4] Ian F. Akyildiz, Tommaso Melodia, Kaushik R.
Chowdhury, “A survey on wireless multimedia sensor
networks”, The International Journal of Computer and
Telecommunications
Networking, Vol. 51 , Iss. 4, March 2007, pp. 921-960.
[5] V. C. Giruka, M. Singhal, J. Royalty, S. Varanasi,
“Security in wireless sensor networks”, Wirel.
Commun. Mob. Comput. 2008; 8:1–24.
[6] T.Kavitha, D.Sridharan, “Security Vulnerabilities In
Wireless Sensor Networks: A Survey”Journal of Information
Assurance and Security, Vol. 5 (2010) 031-044.
[7] Jaydip Sen, “A Survey on Wireless Sensor Network
Security”, International Journal of
Communication Networks and Information Security
(IJCNIS) Vol. 1, No. 2, August 2009.
[8] Chris Karlof, David Wagner, “Secure routing in WSNs:
attacks and ountermeasures”, Ad hoc networks Journal,
vol. 1, Issue 2-3, Sept. 2003, pp.293-315.
[9] G. Padmavathi, D. Shanmugapriya, “A Survey of
Attacks: Security Mechanisms and Challenges in Wireless
Sensor Networks”, (IJCSIS) International Journal of
Computer Science and
V.Vallinayagi1 IJECS Volume 2 Issue 8 August, 2013 Page No.2641-2644
Page 2643
Information Security, Vol. 4, No. 1 & 2, 2009.
[10] Asad Amir Pirzada, Chris McDonald, and Amitava
Datta “Performance Comparison of
Trust-Based
Reactive
Routing
Protocols”,
IEEE
Transactions on Mobile Computing, Vol. 5,
No. 6, June 2006.
[11] Y. Sun, Z. Han, K. J. RAY Liu, “Defense of Trust
Management Vulnerabilities in Distributed
Networks”, IEEE Communications Magazine, February
2008, pp: 112–119.
[12] Sencun Zhu, Sanjeev Setia, Sushil Jajodia. “LEAP:
Efficient Security Mechanisms
V.Vallinayagi1 IJECS Volume 2 Issue 8 August, 2013 Page No.2641-2644
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